Expense is always an important factor in any project. Organizations always find ways to improve their Return on Investment (ROI) in their business. Like other enterprises, building an in-house data labeling team, organizations must solve the cost equation which includes:
- Recruitment and training costs
- Management and maintenance costs
- Salary and remunerations
1. Recruitment and training costs
Recruitment is sometimes misunderstood as a free task, but in reality, it is a time-consuming and expensive task, especially for high-requirement positions.
Specifically, in the field of AI solutions, Data Collection and Annotation are some of the most time-consuming and labor-intensive parts of AI/ML projects. Therefore, most projects require a large number of employees to handle the work on time. The issue here is whether it is worth spending too much time and cost on finding the workforce to process, or not when nowadays there are many outsourcing services with professional staff and reasonable prices. The ROI is the ultimate result of a business while the amount of investment is too high, is it reasonable?
In addition, developing a basic AI model also takes months of training to predict, collect, produce, and develop products. Creating an AI model requires challenges in both time and labor, leading to the final impact on progress and costs. Data solution companies may suffer significant losses if they have to spend too much on training, making them unable to proceed with the project on time.
In another case, short-term training to meet the job deadline causes low AI data quality for their system. The actual results show that the actual cost of developing an official AI model may be much higher than expected. Meanwhile, the task of annotating and labeling data must meet the highest quality and accuracy to enter the process of building an AI model.
2. Management and maintenance costs:
Assuming an AI solution company with a workforce specialized in data acquisition and annotation, however, in the long run, how to manage and maintain that workforce is another challenge. It’s crucial for data service companies to establish a reasonable internal management process, optimize project workflow, achieve efficiency, and improve quality. We always want to optimize costs to increase profits, but in reality, managing and maintaining AI personnel is one of the most expensive tasks. Tangible and intangible costs make up the cost of managing your organization or business. It may seem like they don’t consume too much of your money, but when you list the costs of managing and maintaining employees, you will be surprised to find that the ROI is significantly reduced due to excessive spending on HR management.
The collected data is simply raw data that needs to be processed through labeling, annotation, editing, valuation, etc. The data processing team must identify and manually assign attributes for each element in the data. Then, all images, videos, texts, and sounds that have been processed need to be evaluated and approved. When the results do not meet the standards, they need to be manually adjusted to optimize the quality of the product. Because there are too many stages before the data can be built into a model, optimizing the process incurs a significant cost, and this is truly a tedious and time-consuming job.
3. Salary and remunerations
Only referring to the investment and time, effort that an organization has to put into collecting and annotating data, you can see two main costs:
- The salaries for AI experts, data researchers, data annotators, and leaders evaluating the quality of processed raw data
- The cost of investing in specialized data processing platforms and applications
We have the formula:
Expenses = Number of each position x wage of each position + cost of platform
The average salary for an internal data annotator is quite high (E.g. US: $20, Korea: $7), and if you calculate the total cost for recruitment, training, management, salary, and bonus, you can clearly see that the cost for a data processing workforce is too high.
There are many simpler ways to collect and annotate data from quality resources with minimal cost. To optimize the AI data processing process, a suitable solution that satisfies the labor, time, and cost constraints is needed.
These issues can be summarized and solved with outsourcing services. A team with expertise in data services is ready to provide you with data collection, annotation, and data expansion, helping you ensure quality data for AI model deployment.
Of course, you will have to pay for the provided service, but you can exchange it for many benefits. You only need to pay for the service you request, without spending time looking for employees, working hard to meet deadlines, or ensuring data quality. Additionally, when outsourcing, you will increase your focus on optimizing the final product, or have additional costs for marketing, business development, and other important operations.
Beework.ai is ready to help you solve the difficulties of building a data annotation team at a reasonable cost and with guaranteed quality. Come to us to experience our services.